{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Networks <a class=\"tocSkip\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy:1.13.1\n",
      "Pandas:0.21.0\n",
      "Matplotlib:2.1.0\n",
      "TensorFlow:1.4.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras:2.0.9\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(123)\n",
    "print(\"NumPy:{}\".format(np.__version__))\n",
    "\n",
    "import pandas as pd\n",
    "print(\"Pandas:{}\".format(pd.__version__))\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize']=15,10\n",
    "print(\"Matplotlib:{}\".format(mpl.__version__))\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.set_random_seed(123)\n",
    "print(\"TensorFlow:{}\".format(tf.__version__))\n",
    "\n",
    "import keras\n",
    "print(\"Keras:{}\".format(keras.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASETSLIB_HOME = '../datasetslib'\n",
    "import sys\n",
    "if not DATASETSLIB_HOME in sys.path:\n",
    "    sys.path.append(DATASETSLIB_HOME)\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "import datasetslib\n",
    "\n",
    "from datasetslib import util as dsu\n",
    "datasetslib.datasets_root = os.path.join(os.path.expanduser('~'),'datasets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /home/armando/datasets/mnist/train-images-idx3-ubyte.gz\n",
      "Extracting /home/armando/datasets/mnist/train-labels-idx1-ubyte.gz\n",
      "Extracting /home/armando/datasets/mnist/t10k-images-idx3-ubyte.gz\n",
      "Extracting /home/armando/datasets/mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(os.path.join(datasetslib.datasets_root,'mnist'), one_hot=False)\n",
    "\n",
    "x_train = mnist.train.images\n",
    "x_test = mnist.test.images\n",
    "y_train = mnist.train.labels\n",
    "y_test = mnist.test.labels\n",
    "\n",
    "pixel_size = 28\n",
    "\n",
    "def norm(x):\n",
    "    return (x-0.5)/0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_z = 256\n",
    "z_test = np.random.uniform(-1.0,1.0,size=[8,n_z])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to display the images and labels\n",
    "def display_images(images):\n",
    "    for i in range(images.shape[0]):\n",
    "        plt.subplot(1, 8, i + 1)\n",
    "        plt.imshow(images[i])\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple GAN in TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph hyperparameters\n",
    "g_learning_rate = 0.00001\n",
    "d_learning_rate = 0.01\n",
    "\n",
    "n_x = 784  # number of pixels in the MNIST image as number of inputs\n",
    "\n",
    "# number of hidden layers for generator and discriminator\n",
    "g_n_layers = 3\n",
    "d_n_layers = 1\n",
    "# neurons in each hidden layer\n",
    "g_n_neurons = [256, 512, 1024]\n",
    "d_n_neurons = [256]\n",
    "\n",
    "# define parameter ditionary\n",
    "d_params = {}\n",
    "g_params = {}\n",
    "\n",
    "activation = tf.nn.leaky_relu\n",
    "w_initializer = tf.glorot_uniform_initializer\n",
    "b_initializer = tf.zeros_initializer\n",
    "\n",
    "# define generator\n",
    "\n",
    "z_p = tf.placeholder(dtype=tf.float32, name='z_p', shape=[None, n_z])\n",
    "\n",
    "layer = z_p\n",
    "\n",
    "# add generator network weights, biases and layers\n",
    "with tf.variable_scope('g'):\n",
    "    for i in range(0, g_n_layers):\n",
    "        w_name = 'w_{0:04d}'.format(i)\n",
    "        g_params[w_name] = tf.get_variable(\n",
    "            name=w_name,\n",
    "            shape=[n_z if i == 0 else g_n_neurons[i - 1], g_n_neurons[i]],\n",
    "            initializer=w_initializer())\n",
    "\n",
    "        b_name = 'b_{0:04d}'.format(i)\n",
    "        g_params[b_name] = tf.get_variable(\n",
    "            name=b_name, shape=[g_n_neurons[i]], initializer=b_initializer())\n",
    "\n",
    "        layer = activation(\n",
    "            tf.matmul(layer, g_params[w_name]) + g_params[b_name])\n",
    "\n",
    "    #output (logit) layer\n",
    "    i = g_n_layers\n",
    "    w_name = 'w_{0:04d}'.format(i)\n",
    "    g_params[w_name] = tf.get_variable(\n",
    "        name=w_name,\n",
    "        shape=[g_n_neurons[i - 1], n_x],\n",
    "        initializer=w_initializer())\n",
    "\n",
    "    b_name = 'b_{0:04d}'.format(i)\n",
    "    g_params[b_name] = tf.get_variable(\n",
    "        name=b_name, shape=[n_x], initializer=b_initializer())\n",
    "\n",
    "    g_logit = tf.matmul(layer, g_params[w_name]) + g_params[b_name]\n",
    "    g_model = tf.nn.tanh(g_logit)\n",
    "\n",
    "# define discriminator(s)\n",
    "\n",
    "# add discriminator network weights, biases\n",
    "\n",
    "with tf.variable_scope('d'):\n",
    "    for i in range(0, d_n_layers):\n",
    "        w_name = 'w_{0:04d}'.format(i)\n",
    "        d_params[w_name] = tf.get_variable(\n",
    "            name=w_name,\n",
    "            shape=[n_x if i == 0 else d_n_neurons[i - 1], d_n_neurons[i]],\n",
    "            initializer=w_initializer())\n",
    "\n",
    "        b_name = 'b_{0:04d}'.format(i)\n",
    "        d_params[b_name] = tf.get_variable(\n",
    "            name=b_name, shape=[d_n_neurons[i]], initializer=b_initializer())\n",
    "\n",
    "    #output (logit) layer\n",
    "    i = d_n_layers\n",
    "    w_name = 'w_{0:04d}'.format(i)\n",
    "    d_params[w_name] = tf.get_variable(\n",
    "        name=w_name, shape=[d_n_neurons[i - 1], 1], initializer=w_initializer())\n",
    "\n",
    "    b_name = 'b_{0:04d}'.format(i)\n",
    "    d_params[b_name] = tf.get_variable(\n",
    "        name=b_name, shape=[1], initializer=b_initializer())\n",
    "\n",
    "# define discriminator_real\n",
    "\n",
    "# input real images\n",
    "x_p = tf.placeholder(dtype=tf.float32, name='x_p', shape=[None, n_x])\n",
    "\n",
    "layer = x_p\n",
    "\n",
    "with tf.variable_scope('d'):\n",
    "    for i in range(0, d_n_layers):\n",
    "        w_name = 'w_{0:04d}'.format(i)\n",
    "        b_name = 'b_{0:04d}'.format(i)\n",
    "\n",
    "        layer = activation(\n",
    "            tf.matmul(layer, d_params[w_name]) + d_params[b_name])\n",
    "        layer = tf.nn.dropout(layer,0.7)\n",
    "    #output (logit) layer\n",
    "    i = d_n_layers\n",
    "    w_name = 'w_{0:04d}'.format(i)\n",
    "    b_name = 'b_{0:04d}'.format(i)\n",
    "    d_logit_real = tf.matmul(layer, d_params[w_name]) + d_params[b_name]\n",
    "    d_model_real = tf.nn.sigmoid(d_logit_real)\n",
    "\n",
    "# define discriminator_fake\n",
    "\n",
    "# input generated fake images\n",
    "z = g_model\n",
    "\n",
    "layer = z\n",
    "\n",
    "with tf.variable_scope('d'):\n",
    "    for i in range(0, d_n_layers):\n",
    "        w_name = 'w_{0:04d}'.format(i)\n",
    "        b_name = 'b_{0:04d}'.format(i)\n",
    "\n",
    "        layer = activation(\n",
    "            tf.matmul(layer, d_params[w_name]) + d_params[b_name])\n",
    "        layer = tf.nn.dropout(layer,0.7)\n",
    "    #output (logit) layer\n",
    "    i = d_n_layers\n",
    "    w_name = 'w_{0:04d}'.format(i)\n",
    "    b_name = 'b_{0:04d}'.format(i)\n",
    "\n",
    "    d_logit_fake = tf.matmul(layer, d_params[w_name]) + d_params[b_name]\n",
    "    d_model_fake = tf.nn.sigmoid(d_logit_fake)\n",
    "\n",
    "g_loss = -tf.reduce_mean(tf.log(d_model_fake))\n",
    "d_loss = -tf.reduce_mean(tf.log(d_model_real) + tf.log(1 - d_model_fake))\n",
    "\n",
    "g_optimizer = tf.train.AdamOptimizer(g_learning_rate)\n",
    "d_optimizer = tf.train.GradientDescentOptimizer(d_learning_rate)\n",
    "\n",
    "g_train_op = g_optimizer.minimize(g_loss, var_list=list(g_params.values()))\n",
    "d_train_op = d_optimizer.minimize(d_loss, var_list=list(d_params.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0000   d_loss = 0.374717  g_loss = 1.420409\n"
     ]
    },
    {
     "data": {
      "image/png": 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k++7d6Wr7uUWfk63Ow5LF3xoGq+3jTQlkC/fCR/wdujPPEIytewP7dnesbl15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\npf0qmqCO6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c8c030588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0050   d_loss = 0.490554  g_loss = 2.878258\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88d4d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0100   d_loss = 0.766314  g_loss = 1.976506\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88cd6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0150   d_loss = 0.941174  g_loss = 1.498578\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c860684a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0200   d_loss = 1.043525  g_loss = 1.285293\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c81a1eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0250   d_loss = 1.097761  g_loss = 1.190257\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88dc2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0300   d_loss = 1.138826  g_loss = 1.109527\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88ba7ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0350   d_loss = 1.155886  g_loss = 1.082737\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c81a866d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training hyperparameters\n",
    "\n",
    "n_epochs = 400\n",
    "batch_size = 100\n",
    "n_batches = int(mnist.train.num_examples / batch_size)\n",
    "\n",
    "n_epochs_print = 50\n",
    "\n",
    "with tf.Session() as tfs:\n",
    "    tfs.run(tf.global_variables_initializer())\n",
    "    for epoch in range(n_epochs+1):\n",
    "        epoch_d_loss = 0.0\n",
    "        epoch_g_loss = 0.0\n",
    "        for batch in range(n_batches):\n",
    "            x_batch, _ = mnist.train.next_batch(batch_size)\n",
    "            x_batch = norm(x_batch)\n",
    "            z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])\n",
    "            feed_dict = {x_p: x_batch,z_p: z_batch}\n",
    "            _,batch_d_loss = tfs.run([d_train_op,d_loss], feed_dict=feed_dict)\n",
    "            \n",
    "            z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])\n",
    "            feed_dict={z_p: z_batch}\n",
    "            _,batch_g_loss = tfs.run([g_train_op,g_loss], feed_dict=feed_dict)\n",
    "            \n",
    "            epoch_d_loss += batch_d_loss \n",
    "            epoch_g_loss += batch_g_loss\n",
    "            \n",
    "        if epoch%n_epochs_print == 0:\n",
    "            average_d_loss = epoch_d_loss / n_batches\n",
    "            average_g_loss = epoch_g_loss / n_batches\n",
    "            print('epoch: {0:04d}   d_loss = {1:0.6f}  g_loss = {2:0.6f}'\n",
    "                  .format(epoch,average_d_loss,average_g_loss))\n",
    "            # predict images using generator model trained            \n",
    "            x_pred = tfs.run(g_model,feed_dict={z_p:z_test})\n",
    "            display_images(x_pred.reshape(-1,pixel_size,pixel_size))   \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Simple GAN in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.layers import Dense, Input, LeakyReLU, Dropout\n",
    "from keras.models import Sequential, Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generator:\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "g_0 (Dense)                  (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "g_1 (Dense)                  (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "g_2 (Dense)                  (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "g_out (Dense)                (None, 784)               803600    \n",
      "=================================================================\n",
      "Total params: 1,526,288\n",
      "Trainable params: 1,526,288\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Discriminator:\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "d_0 (Dense)                  (None, 256)               200960    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "d_out (Dense)                (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 201,217\n",
      "Trainable params: 201,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "GAN:\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "z_in (InputLayer)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 784)               1526288   \n",
      "_________________________________________________________________\n",
      "sequential_2 (Sequential)    (None, 1)                 201217    \n",
      "=================================================================\n",
      "Total params: 1,727,505\n",
      "Trainable params: 1,526,288\n",
      "Non-trainable params: 201,217\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# graph hyperparameters\n",
    "g_learning_rate = 0.00001\n",
    "d_learning_rate = 0.01\n",
    "\n",
    "n_x = 784  # number of pixels in the MNIST image as number of inputs\n",
    "\n",
    "# number of hidden layers for generator and discriminator\n",
    "g_n_layers = 3\n",
    "d_n_layers = 1\n",
    "# neurons in each hidden layer\n",
    "g_n_neurons = [256, 512, 1024]\n",
    "d_n_neurons = [256]\n",
    "\n",
    "# define generator\n",
    "\n",
    "g_model = Sequential()\n",
    "g_model.add(Dense(units=g_n_neurons[0], \n",
    "                  input_shape=(n_z,),\n",
    "                  name='g_0'))\n",
    "g_model.add(LeakyReLU())\n",
    "for i in range(1,g_n_layers):\n",
    "    g_model.add(Dense(units=g_n_neurons[i],\n",
    "                      name='g_{}'.format(i)\n",
    "                     ))\n",
    "    g_model.add(LeakyReLU())\n",
    "g_model.add(Dense(units=n_x, activation='tanh',name='g_out'))\n",
    "print('Generator:')\n",
    "g_model.summary()\n",
    "g_model.compile(loss='binary_crossentropy',\n",
    "              optimizer=keras.optimizers.Adam(lr=g_learning_rate)\n",
    "             )\n",
    "\n",
    "# define discriminator\n",
    "\n",
    "d_model = Sequential()\n",
    "d_model.add(Dense(units=d_n_neurons[0],  \n",
    "                  input_shape=(n_x,),\n",
    "                  name='d_0'\n",
    "                 ))\n",
    "d_model.add(LeakyReLU())\n",
    "d_model.add(Dropout(0.3))\n",
    "for i in range(1,d_n_layers):\n",
    "    d_model.add(Dense(units=d_n_neurons[i], \n",
    "                      name='d_{}'.format(i)\n",
    "                     ))\n",
    "    d_model.add(LeakyReLU())\n",
    "    d_model.add(Dropout(0.3))\n",
    "d_model.add(Dense(units=1, activation='sigmoid',name='d_out'))\n",
    "print('Discriminator:')\n",
    "d_model.summary()\n",
    "d_model.compile(loss='binary_crossentropy',\n",
    "              optimizer=keras.optimizers.SGD(lr=d_learning_rate)\n",
    "             )\n",
    "\n",
    "# define GAN network\n",
    "d_model.trainable=False\n",
    "z_in = Input(shape=(n_z,),name='z_in')\n",
    "x_in = g_model(z_in)\n",
    "gan_out = d_model(x_in)\n",
    "\n",
    "gan_model = Model(inputs=z_in,outputs=gan_out,name='gan')\n",
    "print('GAN:')\n",
    "gan_model.summary()\n",
    "gan_model.compile(loss='binary_crossentropy',\n",
    "              optimizer=keras.optimizers.Adam(lr=g_learning_rate)\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0000   d_loss = 0.488523  g_loss = 0.868583\n"
     ]
    },
    {
     "data": {
      "image/png": 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hEWPb6sxzGbuN+d6lr2FOWk6yTeS8D76n6y5eHt6aoU2byZzkbj3TqF32PnJpFA1i7qRX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\nDtZsYruriwbHv7oN50xoDEb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3+KWhpgf2rVaO1lesjZoSp2tLSVcyFD7KXlcG0+StOx+X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\nLifVQPg3WcFawpGVsDe/JX3a+BspdDyQY3r2t1gHndSHvx27le8/ZQ7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L+oAhhBBCCCGE\nEEII26MPGEIIIYQQQgghhLA9UeGwPVNgCSGEEEIIIYQQQvwPisAQQgghhBBCCCGE7dEHDCGEEEII\nIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQ\nQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtge\nfcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQ\nQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQggh\nhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBCCCGE7dEHDCGEEEIIIYQQQtgefcAQQgghhBBC\nCCGE7fkbhzdsd7jBFHQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c7fe4cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0050   d_loss = 0.483392  g_loss = 1.512116\n"
     ]
    },
    {
     "data": {
      "image/png": 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2l5uVsDaprYaMqKFzUV/JJvA5JFZaTsDfJT+bK75zM57D8Oasl2IT+uGRfisG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\niIhcjzcwiIiIiIiIiMj1/hdYPC8gaAgdYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88d81048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0100   d_loss = 0.538450  g_loss = 1.290401\n"
     ]
    },
    {
     "data": {
      "image/png": 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f9Z1XDG13q9I3BwwNmy0HhRegDrmtRnleHv6hrn1rwBw4+4TtPFN0DSvuUlrnqWfdD74R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\nq8Dn7jKe75LExmbbeBEjLzG3BYlQ4s8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c7f6bbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0150   d_loss = 0.560499  g_loss = 1.200036\n"
     ]
    },
    {
     "data": {
      "image/png": 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wJql9fcLXpa2As3aDOz51qZZKdG4ZLP9z6VvuOEc7XqjXARk+fJ1ez8pYX5/q\ntaJlWVb1ENm3c3bnQqxmmKS2Fz+Lv1/8Pbq546baZDur5FyYvQvTDhxD+knapY0kel0RBzVlOe9R\nTB/c9JdKdzzq0S8h1l5pr73XwW1iSxA/68zt35CFfQchFj4m6yu4M/r1ra1tZ/p2GD5qTjv6/3zt\nMJ0xGSmKnIFBRERERERERJ7HGxhERERERERE5Hm8gUFEREREREREntesRREiakBkSC6Ymo+j8+Vg\nbrGat+mMOgVi+2/HvJqTiiXX+dR8rCvR7z1pTdZuHebn9PsvaTU09q2lEHtw8fmwPOCXm93xwRHd\nIZav5NU3DsCaBoH9UncjWIQ5a3aCuYX+wgJYdg5I3pMn8xX1/DIlR1dvH+SswxaVQMnf3TrlDAid\ndeESWN4y0tDq1kDNWau5CGsbFC/agw/ed8Ad2u0xv9Upkdfx79Xy0pT1ZdUcsJJhwwsnwXKfK5Tt\nOU3yno11L2Kl5X/rtUnUFnAPPz0OYp9eKtvQtgtxfW68WdqJlS7FbSvwI8w3fWSf1GO5u2Q1xJYf\nkWPHrI1Yz+CUh6Vd3rQP8fjTbzEen9JjjSaJKT9Vrf+TgXnHaru4je/jcebFN6fB8pagrNMuAXy/\nkPIeHzVgi9NfP361O64/FWvt9Hn4c1j+6TvXueN/zPpfiM1vkFzjyTdcBrGceTKOyDGNsWVoq2M6\nphnqY6jnx+A2rGWl12oqu3SrO764YTjEsvLluN3n0FaIvRiQawC1BphlWdaccdfD8q5xUuvAuRiP\nKyf2lXPhyn6dIFZx0V53HIqjPX1EjTIvXi80g4ic7wTrsXW4cB0sP9tZ2sI/Xf4diBVOle1t1d6O\nEAu8VwjLR5XFnidiPaRwQP5P0l62BmIJ1/tKkH48SnuGem1LxvaE0IZb8XfAuLXyXazZgPW1Btwp\ntTScfbg+A2E8T8DbH8VjQt9npI7CPW+/DLHrv/qBO/Z3xuOFs1U71sUo2B9/z9gLlevVNLmujCpF\ndS/gLZT98eBIfL/8d6Xd9eSSjyCWp9TheuJAH4i9cc+5sJzzr+h1BNXjvfFYr7Vctodiy9fwEqzd\nFE0qrkc4A4OIiIiIiIiIPI83MIiIiIiIiIjI83gDg4iIiIiIiIg8r1lrYOjChr7BKlPunn8e1jc4\nMPp0WL7r+8+44xvWXw6xO4f/2x2/fs9QiB35tLM7PnsG5pNtPbEYll+8c4Q77j8dayGoPXLPePRT\niP1todRqyCjAPMteH1sJcQ7V4j80Qz/jpFJy5/SaF1AzxbDtlE/7CpYXD8N8xMMzs91x1xKsMzG9\n4h/u+EfTJkPsszsecccZ9ofLsqKBAAAcSUlEQVRR39+yLOvjBslPnLl3JMS2XyP5kJ1e3AexXeOl\nhomzF/vIJ5qjDDUvLMvyl5Yq77FXf3jrZch5tyzLCnSS3OMu/7MAYpunqs89ArGe98h3WDNhBMSy\nXsP85WeGjHLHH/+qCD9fheTRXvQ6rrO5t0x1xzl2BsQuvh5z8E15/m1VxPFCOS6WP461SP76Xayh\n82Cnxe6437xrIFY+Q/4PIGs99nLvVIV1TODzaMv2csmXf68ec+BPydrhjv8z4nGIfXP1L9xxqmpe\n+Nq3l/dIQu/2FmXYH+DvrNOuObRzZ6gh+rnUOXQoasx0zM79J+Yr95sv20HDCd0gduyQHMMrN2+H\nmBNHrYNjo091x5n//izm57Vmida8aIpTLef6jHZYZ6P2uzLuUr0SYoFyrDXgbN/ljg9djDXgbv/N\nc+54xpjz8HmmGmKJMtRYaXU1eLRjwL4fy7n+YCU+NHsAXleeWCjH8PAEbX9N9PPox7IDcmzuEsBr\nlL8OmemOL7r3JohVXptYDQx7wbLYHxtrvYU2RK259pctWOdityO/dQp8WL9rjyP14F7YcCrEOn+A\n1zJOjDWfjJ9Tq9XU4WHcfid1lvPWlD7DIGZnKr/ZUnBc5QwMIiIiIiIiIvI83sAgIiIiIiIiIs9r\n0RQSE5gur7U7VNuq2h2wbWjJUpzuMuGDSe44cLgRYq9VK+kmOEvHOpYv7/nJkd4Qm1yC08uPniGf\n9fWiIRCr+J1MFXx+Bba8ue/sV93xi8OxNU1c08r0aXwqr6eNGKYgRrQzU1vtas8LjxjsjredhS1p\nu+ZgK7s7+r3tji/OxRSOkCXrfemdOF3bsrQ2WgbfaCf3Bnt2fhtiP1l/jjueuwbbsZ758np3vHtE\n4lOuAr3L3XH4EE77blNpI3EI7lbSvxJMvShehilcHadvgeWaS2WaetUEbG/bbZZMM718ybUQO7Je\njnOVv8f2eL72eFxLeJp/K0s9UY8ftna88HWW6fnVI7Gt3ay1OAV/9UWSTtinClN71O8sGMd35svF\nY9TOa+S88d3chdqj5Xy3P4RTg88dI2321k2J+e3jAtuT6VyTBvwdy9yxsxvTPb2WHhPcJSlJR2fi\n9rK3QbbtLuOx9WI8aaNZ70kKbvrv8d6mThkPbtGm7RvWU3ArTtlW23/vPg0fe0GObAvO7PcgNuXp\nK91x1z/gNWzCx/tWcJ4wCfSU9GNn+06IHVa6b3fQOklmLWwPy1/NUdM+Y0udj9f2H1S44x4BbPXe\nNSwpzUP6boNYo5KiFNyM18rJAukKHjyHtEQLab+SsvhJQ1eIlWdI+nijhZ9lzL23uePOb6yH2CPL\nZsPy+S/d7o773K7t8ybKOWTDVExTea7zY7A8bYekqvlyMG3MzpDv1WEKCRERERERERG1RbyBQURE\nRERERESexxsYREREREREROR5nq2BoeYg+bT8JGe/5PkdHdEXYsfyML/KDslzM+d+CbGQTx5ra3U2\n8jZJLtgr72CPpFe6nQXLVReUuOOK97F9km+/tFTLXIFtE8eOlPe495HvQKzyWvysxpwsNQ8xbMh5\n9cVew6HZGHIo42m7Yy+Q7yu3D7bS3VuH+cP9M5XcYu3tM+zEvqOj4caosQs+/yksdwtIXYTc5e0g\ntq5M2uN16Ivtep31m2L+PMGNm92x2n6WDJKRz/vZclis/k4JLIfK5RjQaTq22VT38B43dILYa4uf\ndccTR30DYhtHJClfU60/E/DsqSFm6vEjOArbDQYWrHDH+wdgDYzeD+L3GazCHHR8k8S2mQ1/wXpI\nti21UwY9hW3uPvvpNHdc4sdj2UNdZBu6qO94iMVzvIhZmue8q3Uv9l+FLY9LXpf2lc5BrRVqc/zd\nhnpQuRfgurS/q7VOVtSNk1Z2+e9oyfnd8LjirFwb54ekmGnrM9BdOc4Etba8++W6MXQE69zYQ/rD\ncniZtErs+3esuZT1fam1cEkebsPzxkub3AV78Bqp6Ok48uPbkOAWqRdRPQt/Byw6WY7LN287H2LV\nY/A60tHbeCfBuplDYfnPI59yxzMO4jnt0jyplTCoYAfE/vFbeZ3eV6SmBgbw4DmkJVq7bp44yB0P\nzZoDsRLl9+hZi7F1e+kGua7Z9uMKiBX48JjT9UPlOGM4v0TElJo8p5y2DkJLG7Ct87nFco5p/Ay3\n+zdOUK5/Te+fIM7AICIiIiIiIiLP4w0MIiIiIiIiIvK8lp0nHGPbvlADtmZRn5f11mII5RR1iPo6\n4XbYllNtm+bvjFMrj1VK6zzfSmx5FeyQA8vdZsqUvtVTcEpP93ekndFVl78LsT/tk6mehQvws1m2\ndm8pGS0OlVZKrY0vO9sdFz6L0yHtVYNg+cdTfuSOD67FtJ7crfK9v3/bVIip07ernTqItdNSTxqV\n73r20BkQu77wMnfc9WHcfuvWn+yOnY0YS1Q4BdMXWyPfYJmqe+CEQojlv7jIHW94EKff9psm07vV\n1oeWZVlOLzyuBHZK+lu4BFOEQgcOuuM1t5VDbMoe2S66ZWHbxGVvYivePHW6uXasUFNDTNMmwyHv\nTfO0rMh0KH8XaYeqTvfV+T9chstlMrWx74OrIdYc7TQrJuF2om43dZdgb8RGw3H7kvUXysJhPCal\nYsqmJ8XzdyqPLV6K6Z6Osv+prdotK3Jaf8zvFw/T62ix3DnSznf7rZhO0uPvkqLoVHSH2LbzsO18\nt9UbZMHrLdePk3p8D3252vBALY00FOM0bO25DRfiFP/sHbJ/7hmeD7HSp5Tjgf6aX2HbbPU6LvOP\n1RBylJhfu4acPV8+T6faVnosiEHEvl1fH+WReL7MzsDz5d8PSfr6493fgdj4cmyDbu3XWh3HyHS+\nHnAn/i6ZWiPrd82jQyD2rdEPueMHyr6C2MtrTrYIrZ8m13h9J+ttzRPjL8Rj7+LrHnbH8xvwd+uV\nv5Ttp+tbuP87B+S8NekJbO1b4MOU9Ox/y3nCuMfrxxzlOHdft1kQGjNnEix3mSuPrR6P58miy2Vf\ny/97cr5HFWdgEBEREREREZHn8QYGEREREREREXkeb2AQERERERERkee1bA0MUxuXOPJBVc4+zDWD\n9qhaq1T1PYO790IoOLibPE3LXzvaoRcs75gguZX9H9kV9bPOWHYmhHzbJV+pxzqsU6DXLYg1d93f\nFz8btNJrrTnQljmP0V+F6zbvqZ7uuOOGGog5q6Xd1FWvXQ6xcKa0KGvohbUztp6Pu9JFoz51x1/8\nSsuFPbZZPlsJvk7Oa/K8ptaXuk34tXoKwT2SGxsow1aeep0G+j/hlbLuOzjY5vL1KqlHUu18BLFv\n95c2VyVjcf/31+F+POXDV93xbbfeCLG8BZvdceXvN0Bs/WnSXrf2WsyXbL8Zaz+E1e1Gy+eOuV2Y\nR/Ph9eOiqe5FoHe5PE5pK2xZlrXtB7J+uz6J9THCDv7tat2NeOrJQM6r1pb2yGCsTZCh7K/5H2Br\ny1PeucUdrx+N9XQG5Mv29lVJP/wASsvQuJhqAHhRgue10DJsMRo68yR3vPYaXF/9Hsc6XDu/ITUM\nOj2yCGJ/2CCtba9cgi3wBneUFoYHL8F6Lk4NXmeEg0prbu1vVFsEd1qIecdV4+X81mENtvfu+fR6\nWHa8vm6TSK174cvFdsR2prKP68fIRuU77FsOIZ9Wd2bvKLluLP0P1ig42kuO4YfxMs3Kv0DaPO85\nJQNivf53Iyw71fvccXA8rr/aJbJdFNjZEHvs28+444dvOxFi8exBiR4PvcJ0ragLDxvojvMv2wyx\nMculNkGGjTXshj3zJSy/ME9an1fc+hnE1OOrvl36lHPI4WHdIFYzEI9RPZ+U49nAe/G8eFX5D93x\n3EGvQOzng99zx6/4OkPM88f+FElW3QtQitfoo1d8zx0HpuLvgMIFUqfE1q7f1z3U2x2PzPkTxFY1\n4rnbr7Ru1q+BgOH39+Rv4zms3Tjc7mqUEoN97sF9y66T31epaFTLGRhERERERERE5Hm8gUFERERE\nREREnteyKSSqOKaBQjqFNt03ov2oSm8NqL5nGF8n853P5SWzcSpeYx6+x5GO0rpq6rvPQ+zGG2T6\nb7/7cYropsvldQPvf26ZxDr1G1JGrNhTT1ozPWWi3Zuy7BimTpmmp2du2grLFR/irrS6UKaL3v3x\n0xB7sA9O31SpqSBOdU3Ux1kWrs+ItBA1PaoNp4zYWTK1U512/XWcEbJewvdi2lFVUKZpL2rA6f+l\n46VtoX4U+9lr2IJqeJZMD/7wcUwH2NBY645vPmksxGqvK5OFndg6K+LvUta9L1drGdcMLUK9Qp0y\n6e+AaTdd35Zp2Kv/iG1o+03UUkoaYzxu6seSjnIMsA/i9541fyUsh5SpunYOnm+Wnv+I+kyIndVe\npsUvycfWedqniV1rnjasHN/9+djKsqa/fO+TT5sDsfPPwfXVRUlH3TEZv6/KDJkGvuy0ZyEWtOSx\n42zcx9c8NRiW+10n09D1qfprZ5zqjk+oxFSF8CzJT6i78SDEssbica1NUfbPiDSCOkkF0dMxHeWY\nuX8YHkfyduTB8rmTPnbHQ3+1GWLFfjm+L23oAbFt58gU8rvL5kPsyuevgGU7Q641jp2A56ICHx47\nVI5yRDiea0FbSaVNxxQSE71Nt7VE0kQeXvsBhNREH5/2f8G3FGMKwms7RspjMzFFKByMfqQue0U5\nb1yM15yNueWwrLbXDHTBVJD3B/3THetNuV/eISnOgVxsLd2WrhdUgU5Ke3Y9FTPBlEVnHaaCZfxB\nvvcbnnwZYqdkSaphlwCe8+tDktLWwY8pR73e+CksV27S0pWisAO4TarHmNAKbOOa+UB/WO5yt3wf\n4Sq8NnVOkHQXq2p7TJ8lHpyBQURERERERESexxsYREREREREROR5vIFBRERERERERJ7nnRoYpjaq\nhlaAem6zc/AQPlatexEy5Otp76+2I23siLmy7aqxNVlxpbzn27WYT606OATb4fT8rbRmbDKrSv0O\n4shRbqt1L2IWT+0VJT9SzRGzLMvyFeA2svlRyaNt78MWfL4cqUsQOoIt8Jqqe4EfSNlm9b8j3Vvm\nxtNW2fC8pupeqPxK66rg/ZiP/p1hv3DHubswi7QoR3IEG08fCLH+mR9bKNeKZq8j+ct67ml4xX79\n4dEp31WoLvaWcWnP1P6zFNuUWVskHzN3E8Yial7EerzVW11ukzzWxlMqIbZnGLbp7fp3aW+572zM\nj1+vtEYbmoX/51AekO1i+zdx2+r+WfTTO88LkfWzQkoa8FsjekLsb5eMgeWZU6a5447aZqc6Gsbv\n+YR3r3fHvQfgY9ttwfx7aKOqKfhKPuyPRn0CsZlvtXfHW7LwmqMojC1625QYzyGhw7X4D6fLueDc\niXg8H12A7TLn10or4wMO1h/qniHn9lOyN0Ns+rzz3PH5530FsTveew2WJ/9OtqGQtu05YTk3qfVW\nLMuyHrtgnCz4tkAsnmvKUF1d0w9KU6aaHqNnTYblVRc/6o5rQ3idcUzb1gaOlWuEg1Oj79fWMYxt\n/I20dt56P153vP2tP8LypLelpk5jz1KIhZTKF1k21jvYNl/qqJQfwRoGbVVz1I0LfCB1D6f+N9a5\n+eFds93xFe2xBsWWoOz0XxzDA0C/m77AN1HbQx/DbdtfLNc9Ts0+iFnKccRfhttSt1u1a8qQPNaZ\nVQgh+wI536TiFwlnYBARERERERGR5/EGBhERERERERF5Hm9gEBEREREREZHneacGhik/UcvPswNx\nfGxDbp9a0yA0HHPX64oktvVCfN68MdNg+cLPpffuX/8yGmJdF62WBe1zO/H00Fb/jnjqAyRYO6PF\nJOPzJlo/QVM/7jRYDtRLrlfNQMwjfH3ig7B8zqyfu+N7Kkbgx3GUuhdxfDZ/v76w7KzdILFSzFNz\n9u6N+jrq/uPZXPhEa3gcR+0PqK0zbwnEulVLbnN43SaIqZmpP3xqFsQqM6LXvNDd20+2k3AwjmOD\nSTrs88li+FvVfcWyLMtfIf3J2+3Dbcafn4fPPXAwprdXa9tYlmXZOVLTpPp2rIPz58F/huX7Xvy2\nO67rhP+vkGFj7rNq5r4z3HGPRzB3PuTVfbsFBTp3csfBnbsg1uklyTV2DmEtrdA4rE105YNyfO91\n+TqIndZhszse0G47xJ47W9b7byaOglj5QlxftlKHK7QVX6fLC3JdMfMlPE85e+TzdFvZhmteJHgd\noNfAOVqc5Y7vKF0EscYw7pvt2i93xxMengix714zT56nFa8o6XHAHd/46Q8gVv4E/h0ln0qeu78D\n5pwvvUM++y1rLodYvlKTp02dFzT+gViPyDHsI3aWrPuKWxZDbMdYqXvxq6qxEPtr+Xuw/NlyqXnU\nz6fVKbDleL/hgaEQuuqCue749a1Yl2vss7fBcu9jsu0FNmAti9t3ynni1tK5EOs5RWropHnltLRV\n9MZKWJ79plyfTH/ymxD74IzH3fG4j66C2IASPE84NdFrpzX2l9onvk8OQEy9lrEzsTZTcJNWP2f4\nifK8sRshFDqWpOvYKDgDg4iIiIiIiIg8jzcwiIiIiIiIiMjzWjaFxNQG0kCd6u3s16bIaNMG1eny\n/q6dIRbcWiWxJTiNLLdnV2UJW7VetOQnsHxOD3nu6BtxGu/0GcPks5TjNH9r9x53qE8/DtUb2h/a\n2n2nsEwHVNNiLMvcFsqTkjG1MUktRHNnL4XlmitOccdFq7DdVakfd6UnRz/tjn/2lx9CrOIqbQph\njJw166PHDCkjOs+mjXiJtg2Flq+O8kC09ViJ9i/VUR9br7V1TsW+qqfbcd3/H2edTHUsXofTHhM9\nAunH7IDSpuzEsh0Qu3cMtk2zMySt7JPJmKKotr2rDWEqyhf7ZBroX1e8ALEJA86H5bDSos+0ranT\npi0rvlbEXqenjahO/UBa5y0cgimCJWO1aea2pGnUz8B0gPe+caY7nrdLa4e8Ra45Qg2YphJB2y5V\njrJO9PUVz/nP115aruqtm2Omp2p4RcJpiJgWkjVHUgcunDQJYvUleC12sFLesx1e0lkZthxZFk04\nGWKNZ0gb9opnV0FMP66o+2NY+xs3N8r5p+CH2vrsJccKa422benXXUoqry8T94VQAx6D0o0pZUSn\nftfOqFMgduVtcm1f8CGmlX7n8EhY7ndMrvkiUuB9sg31nYKpq6tHSspbzpOYLlQ0eyEsh5Rtwa+t\nsw0XyW+PGzrg7xfLltS5iN8hWsvcZBwvAp06JvS8ZpXgb9NE6SmLqopb98DyWXdJ+mLlrZ9DzPHj\nuUg9zwd6YWvw4Hz8fRP182jr2V9YgK/jl+23uVsscwYGEREREREREXkeb2AQERERERERkefxBgYR\nEREREREReV7L1sCINbcojnZY/v7YajK0Xlq+OLswl0jN99LzDG3leV3eL4LYgcsxz2jFJGkj82X2\nSRDLqFdylPTcOyXPMKLmhelvNtSJCAcbo8YoPnrud9FMyTkMdO0CscH/wpZp/X8jOZH9atdALGTI\nr1NrmKRd/ZJUauacxETdUbwClg9qdS4OKvvuhB5nWqnGmhcppmyXtpZ/euB0qaO09qPuEOuz7jN8\nHeW5J72IefYZtfL/DEdL8Njf50U5Rv1k4SiIOSOwXaDvI8yvjkZvm9aaamCY6HUvjJRjkL6P+ZQW\nzBFn6hTUizie9ZNw3Qv4AN49HifE8Pfk/hPbqLZXagJYlmWV1sl13M5J2N72gvZfuuNzX8LzRDtb\ntqE7Hjk95s/j7MZr2sevv9QdZ9ZptTSqlXpMTa0z5TwVDrfszwSv8H+4DJbzlO8onrpJxnOyD88h\n+y+V3x7Z1fj+ev0TlVOzD/9BPe7swBar6rbQVA0DOF5on1XdZkx1lIK7dlteFz5dWtbaC5YZHpl6\net2miptlOWILMGxbEe1PY6VtZ3pb+Zb8fjgDg4iIiIiIiIg8jzcwiIiIiIiIiMjzmnVumE+bbheq\nrZUF05Q2PaZOXdLSKZxV66yotOmb6rSmiHaDyvT9/HU4zbLwJpyeFQ7KZ/DF0c7SMswm9WVnw3Lo\niLTZi+u7aiuaI8VAed1g1XYIVUzENomWuq072gRDw+czpo2Yph/H8zenSToGUD+nYeqib8gADC3D\nabSpVh/G9XfGYz+H5e7/s1hZSk6KUFtqlRrRCizWaZGGbea4KNvllruGQyh/s8R63/kpPk1/f2Wd\n9bkdp6mr9DZ3W24d4o67z8f1HvgCUxahOaQhRTEpaQWtjH7tElbOx/Hsb74T+rnjWFszxy0dj+9p\nyrSvdJ72CSzfNU2ODxHt7tVrhHDix6bA+5KyHDI8Lh56ipIvN1few5ByoB+rvGL9w5ii03fSwiiP\n1CTrnBHHe4SUVJDjSuUzXD/5siXdA35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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c7f592b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0200   d_loss = 0.586289  g_loss = 1.097394\n"
     ]
    },
    {
     "data": {
      "image/png": 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JYYZxctn8iPyemXcAz2Fbm2XshpJFMLawfhz0m8oL5PW24mu0yc1HvtQcKq5W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c74c834e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0250   d_loss = 0.627239  g_loss = 0.942356\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c7463ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0300   d_loss = 0.647910  g_loss = 0.863883\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c74c90a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0350   d_loss = 0.659564  g_loss = 0.830357\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c88d99470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training hyperparameters\n",
    "\n",
    "n_epochs = 400\n",
    "batch_size = 100\n",
    "n_batches = int(mnist.train.num_examples / batch_size)\n",
    "n_epochs_print = 50\n",
    "\n",
    "for epoch in range(n_epochs+1):\n",
    "    epoch_d_loss = 0.0\n",
    "    epoch_g_loss = 0.0\n",
    "    for batch in range(n_batches):\n",
    "        x_batch, _ = mnist.train.next_batch(batch_size)\n",
    "        x_batch = norm(x_batch)\n",
    "        z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])\n",
    "        g_batch = g_model.predict(z_batch)\n",
    "        \n",
    "        x_in = np.concatenate([x_batch,g_batch])\n",
    "        \n",
    "        y_out = np.ones(batch_size*2)\n",
    "        y_out[:batch_size]=0.9\n",
    "        y_out[batch_size:]=0.1\n",
    "        \n",
    "        d_model.trainable=True\n",
    "        batch_d_loss = d_model.train_on_batch(x_in,y_out)\n",
    "\n",
    "        z_batch = np.random.uniform(-1.0,1.0,size=[batch_size,n_z])\n",
    "        x_in=z_batch\n",
    "        \n",
    "        y_out = np.ones(batch_size)\n",
    "            \n",
    "        d_model.trainable=False\n",
    "        batch_g_loss = gan_model.train_on_batch(x_in,y_out)\n",
    "        \n",
    "        epoch_d_loss += batch_d_loss \n",
    "        epoch_g_loss += batch_g_loss \n",
    "    if epoch%n_epochs_print == 0:\n",
    "        average_d_loss = epoch_d_loss / n_batches\n",
    "        average_g_loss = epoch_g_loss / n_batches\n",
    "        print('epoch: {0:04d}   d_loss = {1:0.6f}  g_loss = {2:0.6f}'\n",
    "              .format(epoch,average_d_loss,average_g_loss))\n",
    "        # predict images using generator model trained            \n",
    "        x_pred = g_model.predict(z_test)\n",
    "        display_images(x_pred.reshape(-1,pixel_size,pixel_size))   "
   ]
  }
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